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2025-09-21 20:19:15 +08:00
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pcdet/ops/pointnet2/pointnet2_stack/pointnet2_utils.py Normal file
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import torch
import torch.nn as nn
from torch.autograd import Function, Variable
from . import pointnet2_stack_cuda as pointnet2
class BallQuery(Function):
@staticmethod
def forward(ctx, radius: float, nsample: int, xyz: torch.Tensor, xyz_batch_cnt: torch.Tensor,
new_xyz: torch.Tensor, new_xyz_batch_cnt):
"""
Args:
ctx:
radius: float, radius of the balls
nsample: int, maximum number of features in the balls
xyz: (N1 + N2 ..., 3) xyz coordinates of the features
xyz_batch_cnt: (batch_size), [N1, N2, ...]
new_xyz: (M1 + M2 ..., 3) centers of the ball query
new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
Returns:
idx: (M1 + M2, nsample) tensor with the indicies of the features that form the query balls
"""
assert new_xyz.is_contiguous()
assert new_xyz_batch_cnt.is_contiguous()
assert xyz.is_contiguous()
assert xyz_batch_cnt.is_contiguous()
B = xyz_batch_cnt.shape[0]
M = new_xyz.shape[0]
idx = torch.cuda.IntTensor(M, nsample).zero_()
pointnet2.ball_query_wrapper(B, M, radius, nsample, new_xyz, new_xyz_batch_cnt, xyz, xyz_batch_cnt, idx)
empty_ball_mask = (idx[:, 0] == -1)
idx[empty_ball_mask] = 0
ctx.mark_non_differentiable(idx)
ctx.mark_non_differentiable(empty_ball_mask)
return idx, empty_ball_mask
@staticmethod
def backward(ctx, a=None, b=None):
return None, None, None, None, None, None
ball_query = BallQuery.apply
class GroupingOperation(Function):
@staticmethod
def forward(ctx, features: torch.Tensor, features_batch_cnt: torch.Tensor,
idx: torch.Tensor, idx_batch_cnt: torch.Tensor):
"""
Args:
ctx:
features: (N1 + N2 ..., C) tensor of features to group
features_batch_cnt: (batch_size) [N1 + N2 ...] tensor containing the indicies of features to group with
idx: (M1 + M2 ..., nsample) tensor containing the indicies of features to group with
idx_batch_cnt: (batch_size) [M1 + M2 ...] tensor containing the indicies of features to group with
Returns:
output: (M1 + M2, C, nsample) tensor
"""
assert features.is_contiguous()
assert features_batch_cnt.is_contiguous()
assert idx.is_contiguous()
assert idx_batch_cnt.is_contiguous()
assert features.shape[0] == features_batch_cnt.sum(), \
'features: %s, features_batch_cnt: %s' % (str(features.shape), str(features_batch_cnt))
assert idx.shape[0] == idx_batch_cnt.sum(), \
'idx: %s, idx_batch_cnt: %s' % (str(idx.shape), str(idx_batch_cnt))
M, nsample = idx.size()
N, C = features.size()
B = idx_batch_cnt.shape[0]
output = torch.cuda.FloatTensor(M, C, nsample)
pointnet2.group_points_wrapper(B, M, C, nsample, features, features_batch_cnt, idx, idx_batch_cnt, output)
ctx.for_backwards = (B, N, idx, features_batch_cnt, idx_batch_cnt)
return output
@staticmethod
def backward(ctx, grad_out: torch.Tensor):
"""
Args:
ctx:
grad_out: (M1 + M2 ..., C, nsample) tensor of the gradients of the output from forward
Returns:
grad_features: (N1 + N2 ..., C) gradient of the features
"""
B, N, idx, features_batch_cnt, idx_batch_cnt = ctx.for_backwards
M, C, nsample = grad_out.size()
grad_features = Variable(torch.cuda.FloatTensor(N, C).zero_())
grad_out_data = grad_out.data.contiguous()
pointnet2.group_points_grad_wrapper(B, M, C, N, nsample, grad_out_data, idx,
idx_batch_cnt, features_batch_cnt, grad_features.data)
return grad_features, None, None, None
grouping_operation = GroupingOperation.apply
class QueryAndGroup(nn.Module):
def __init__(self, radius: float, nsample: int, use_xyz: bool = True):
"""
Args:
radius: float, radius of ball
nsample: int, maximum number of features to gather in the ball
use_xyz:
"""
super().__init__()
self.radius, self.nsample, self.use_xyz = radius, nsample, use_xyz
def forward(self, xyz: torch.Tensor, xyz_batch_cnt: torch.Tensor,
new_xyz: torch.Tensor, new_xyz_batch_cnt: torch.Tensor,
features: torch.Tensor = None):
"""
Args:
xyz: (N1 + N2 ..., 3) xyz coordinates of the features
xyz_batch_cnt: (batch_size), [N1, N2, ...]
new_xyz: (M1 + M2 ..., 3) centers of the ball query
new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
features: (N1 + N2 ..., C) tensor of features to group
Returns:
new_features: (M1 + M2, C, nsample) tensor
"""
assert xyz.shape[0] == xyz_batch_cnt.sum(), 'xyz: %s, xyz_batch_cnt: %s' % (str(xyz.shape), str(new_xyz_batch_cnt))
assert new_xyz.shape[0] == new_xyz_batch_cnt.sum(), \
'new_xyz: %s, new_xyz_batch_cnt: %s' % (str(new_xyz.shape), str(new_xyz_batch_cnt))
# idx: (M1 + M2 ..., nsample), empty_ball_mask: (M1 + M2 ...)
idx, empty_ball_mask = ball_query(self.radius, self.nsample, xyz, xyz_batch_cnt, new_xyz, new_xyz_batch_cnt)
grouped_xyz = grouping_operation(xyz, xyz_batch_cnt, idx, new_xyz_batch_cnt) # (M1 + M2, 3, nsample)
grouped_xyz -= new_xyz.unsqueeze(-1)
grouped_xyz[empty_ball_mask] = 0
if features is not None:
grouped_features = grouping_operation(features, xyz_batch_cnt, idx, new_xyz_batch_cnt) # (M1 + M2, C, nsample)
grouped_features[empty_ball_mask] = 0
if self.use_xyz:
new_features = torch.cat([grouped_xyz, grouped_features], dim=1) # (M1 + M2 ..., C + 3, nsample)
else:
new_features = grouped_features
else:
assert self.use_xyz, "Cannot have not features and not use xyz as a feature!"
new_features = grouped_xyz
return new_features, idx
class FarthestPointSampling(Function):
@staticmethod
def forward(ctx, xyz: torch.Tensor, npoint: int):
"""
Args:
ctx:
xyz: (B, N, 3) where N > npoint
npoint: int, number of features in the sampled set
Returns:
output: (B, npoint) tensor containing the set
"""
assert xyz.is_contiguous()
B, N, _ = xyz.size()
output = torch.cuda.IntTensor(B, npoint)
temp = torch.cuda.FloatTensor(B, N).fill_(1e10)
pointnet2.farthest_point_sampling_wrapper(B, N, npoint, xyz, temp, output)
return output
@staticmethod
def backward(xyz, a=None):
return None, None
farthest_point_sample = furthest_point_sample = FarthestPointSampling.apply
class StackFarthestPointSampling(Function):
@staticmethod
def forward(ctx, xyz, xyz_batch_cnt, npoint):
"""
Args:
ctx:
xyz: (N1 + N2 + ..., 3) where N > npoint
xyz_batch_cnt: [N1, N2, ...]
npoint: int, number of features in the sampled set
Returns:
output: (npoint.sum()) tensor containing the set,
npoint: (M1, M2, ...)
"""
assert xyz.is_contiguous() and xyz.shape[1] == 3
batch_size = xyz_batch_cnt.__len__()
if not isinstance(npoint, torch.Tensor):
if not isinstance(npoint, list):
npoint = [npoint for i in range(batch_size)]
npoint = torch.tensor(npoint, device=xyz.device).int()
N, _ = xyz.size()
temp = torch.cuda.FloatTensor(N).fill_(1e10)
output = torch.cuda.IntTensor(npoint.sum().item())
pointnet2.stack_farthest_point_sampling_wrapper(xyz, temp, xyz_batch_cnt, output, npoint)
return output
@staticmethod
def backward(xyz, a=None):
return None, None
stack_farthest_point_sample = StackFarthestPointSampling.apply
class ThreeNN(Function):
@staticmethod
def forward(ctx, unknown, unknown_batch_cnt, known, known_batch_cnt):
"""
Args:
ctx:
unknown: (N1 + N2..., 3)
unknown_batch_cnt: (batch_size), [N1, N2, ...]
known: (M1 + M2..., 3)
known_batch_cnt: (batch_size), [M1, M2, ...]
Returns:
dist: (N1 + N2 ..., 3) l2 distance to the three nearest neighbors
idx: (N1 + N2 ..., 3) index of the three nearest neighbors, range [0, M1+M2+...]
"""
assert unknown.shape.__len__() == 2 and unknown.shape[1] == 3
assert known.shape.__len__() == 2 and known.shape[1] == 3
assert unknown_batch_cnt.__len__() == known_batch_cnt.__len__()
dist2 = unknown.new_zeros(unknown.shape)
idx = unknown_batch_cnt.new_zeros(unknown.shape).int()
pointnet2.three_nn_wrapper(
unknown.contiguous(), unknown_batch_cnt.contiguous(),
known.contiguous(), known_batch_cnt.contiguous(), dist2, idx
)
return torch.sqrt(dist2), idx
@staticmethod
def backward(ctx, a=None, b=None):
return None, None
three_nn = ThreeNN.apply
class ThreeInterpolate(Function):
@staticmethod
def forward(ctx, features: torch.Tensor, idx: torch.Tensor, weight: torch.Tensor):
"""
Args:
ctx:
features: (M1 + M2 ..., C)
idx: [N1 + N2 ..., 3]
weight: [N1 + N2 ..., 3]
Returns:
out_tensor: (N1 + N2 ..., C)
"""
assert idx.shape[0] == weight.shape[0] and idx.shape[1] == weight.shape[1] == 3
ctx.three_interpolate_for_backward = (idx, weight, features.shape[0])
output = features.new_zeros((idx.shape[0], features.shape[1]))
pointnet2.three_interpolate_wrapper(features.contiguous(), idx.contiguous(), weight.contiguous(), output)
return output
@staticmethod
def backward(ctx, grad_out: torch.Tensor):
"""
Args:
ctx:
grad_out: (N1 + N2 ..., C)
Returns:
grad_features: (M1 + M2 ..., C)
"""
idx, weight, M = ctx.three_interpolate_for_backward
grad_features = grad_out.new_zeros((M, grad_out.shape[1]))
pointnet2.three_interpolate_grad_wrapper(
grad_out.contiguous(), idx.contiguous(), weight.contiguous(), grad_features
)
return grad_features, None, None
three_interpolate = ThreeInterpolate.apply
class ThreeNNForVectorPoolByTwoStep(Function):
@staticmethod
def forward(ctx, support_xyz, xyz_batch_cnt, new_xyz, new_xyz_grid_centers, new_xyz_batch_cnt,
max_neighbour_distance, nsample, neighbor_type, avg_length_of_neighbor_idxs, num_total_grids,
neighbor_distance_multiplier):
"""
Args:
ctx:
// support_xyz: (N1 + N2 ..., 3) xyz coordinates of the features
// xyz_batch_cnt: (batch_size), [N1, N2, ...]
// new_xyz: (M1 + M2 ..., 3) centers of the ball query
// new_xyz_grid_centers: (M1 + M2 ..., num_total_grids, 3) grids centers of each grid
// new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
// nsample: find all (-1), find limited number(>0)
// neighbor_type: 1: ball, others: cube
// neighbor_distance_multiplier: query_distance = neighbor_distance_multiplier * max_neighbour_distance
Returns:
// new_xyz_grid_idxs: (M1 + M2 ..., num_total_grids, 3) three-nn
// new_xyz_grid_dist2: (M1 + M2 ..., num_total_grids, 3) square of dist of three-nn
"""
num_new_xyz = new_xyz.shape[0]
new_xyz_grid_dist2 = new_xyz_grid_centers.new_zeros(new_xyz_grid_centers.shape)
new_xyz_grid_idxs = new_xyz_grid_centers.new_zeros(new_xyz_grid_centers.shape).int().fill_(-1)
while True:
num_max_sum_points = avg_length_of_neighbor_idxs * num_new_xyz
stack_neighbor_idxs = new_xyz_grid_idxs.new_zeros(num_max_sum_points)
start_len = new_xyz_grid_idxs.new_zeros(num_new_xyz, 2).int()
cumsum = new_xyz_grid_idxs.new_zeros(1)
pointnet2.query_stacked_local_neighbor_idxs_wrapper_stack(
support_xyz.contiguous(), xyz_batch_cnt.contiguous(),
new_xyz.contiguous(), new_xyz_batch_cnt.contiguous(),
stack_neighbor_idxs.contiguous(), start_len.contiguous(), cumsum,
avg_length_of_neighbor_idxs, max_neighbour_distance * neighbor_distance_multiplier,
nsample, neighbor_type
)
avg_length_of_neighbor_idxs = cumsum[0].item() // num_new_xyz + int(cumsum[0].item() % num_new_xyz > 0)
if cumsum[0] <= num_max_sum_points:
break
stack_neighbor_idxs = stack_neighbor_idxs[:cumsum[0]]
pointnet2.query_three_nn_by_stacked_local_idxs_wrapper_stack(
support_xyz, new_xyz, new_xyz_grid_centers, new_xyz_grid_idxs, new_xyz_grid_dist2,
stack_neighbor_idxs, start_len, num_new_xyz, num_total_grids
)
return torch.sqrt(new_xyz_grid_dist2), new_xyz_grid_idxs, torch.tensor(avg_length_of_neighbor_idxs)
three_nn_for_vector_pool_by_two_step = ThreeNNForVectorPoolByTwoStep.apply
class VectorPoolWithVoxelQuery(Function):
@staticmethod
def forward(ctx, support_xyz: torch.Tensor, xyz_batch_cnt: torch.Tensor, support_features: torch.Tensor,
new_xyz: torch.Tensor, new_xyz_batch_cnt: torch.Tensor, num_grid_x, num_grid_y, num_grid_z,
max_neighbour_distance, num_c_out_each_grid, use_xyz,
num_mean_points_per_grid=100, nsample=-1, neighbor_type=0, pooling_type=0):
"""
Args:
ctx:
support_xyz: (N1 + N2 ..., 3) xyz coordinates of the features
xyz_batch_cnt: (batch_size), [N1, N2, ...]
support_features: (N1 + N2 ..., C)
new_xyz: (M1 + M2 ..., 3) centers of new positions
new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
num_grid_x: number of grids in each local area centered at new_xyz
num_grid_y:
num_grid_z:
max_neighbour_distance:
num_c_out_each_grid:
use_xyz:
neighbor_type: 1: ball, others: cube:
pooling_type: 0: avg_pool, 1: random choice
Returns:
new_features: (M1 + M2 ..., num_c_out)
"""
assert support_xyz.is_contiguous()
assert support_features.is_contiguous()
assert xyz_batch_cnt.is_contiguous()
assert new_xyz.is_contiguous()
assert new_xyz_batch_cnt.is_contiguous()
num_total_grids = num_grid_x * num_grid_y * num_grid_z
num_c_out = num_c_out_each_grid * num_total_grids
N, num_c_in = support_features.shape
M = new_xyz.shape[0]
assert num_c_in % num_c_out_each_grid == 0, \
f'the input channels ({num_c_in}) should be an integral multiple of num_c_out_each_grid({num_c_out_each_grid})'
while True:
new_features = support_features.new_zeros((M, num_c_out))
new_local_xyz = support_features.new_zeros((M, 3 * num_total_grids))
point_cnt_of_grid = xyz_batch_cnt.new_zeros((M, num_total_grids))
num_max_sum_points = num_mean_points_per_grid * M
grouped_idxs = xyz_batch_cnt.new_zeros((num_max_sum_points, 3))
num_cum_sum = pointnet2.vector_pool_wrapper(
support_xyz, xyz_batch_cnt, support_features, new_xyz, new_xyz_batch_cnt,
new_features, new_local_xyz, point_cnt_of_grid, grouped_idxs,
num_grid_x, num_grid_y, num_grid_z, max_neighbour_distance, use_xyz,
num_max_sum_points, nsample, neighbor_type, pooling_type
)
num_mean_points_per_grid = num_cum_sum // M + int(num_cum_sum % M > 0)
if num_cum_sum <= num_max_sum_points:
break
grouped_idxs = grouped_idxs[:num_cum_sum]
normalizer = torch.clamp_min(point_cnt_of_grid[:, :, None].float(), min=1e-6)
new_features = (new_features.view(-1, num_total_grids, num_c_out_each_grid) / normalizer).view(-1, num_c_out)
if use_xyz:
new_local_xyz = (new_local_xyz.view(-1, num_total_grids, 3) / normalizer).view(-1, num_total_grids * 3)
num_mean_points_per_grid = torch.Tensor([num_mean_points_per_grid]).int()
nsample = torch.Tensor([nsample]).int()
ctx.vector_pool_for_backward = (point_cnt_of_grid, grouped_idxs, N, num_c_in)
ctx.mark_non_differentiable(new_local_xyz, num_mean_points_per_grid, nsample, point_cnt_of_grid)
return new_features, new_local_xyz, num_mean_points_per_grid, point_cnt_of_grid
@staticmethod
def backward(ctx, grad_new_features: torch.Tensor, grad_local_xyz: torch.Tensor, grad_num_cum_sum, grad_point_cnt_of_grid):
"""
Args:
ctx:
grad_new_features: (M1 + M2 ..., num_c_out), num_c_out = num_c_out_each_grid * num_total_grids
Returns:
grad_support_features: (N1 + N2 ..., C_in)
"""
point_cnt_of_grid, grouped_idxs, N, num_c_in = ctx.vector_pool_for_backward
grad_support_features = grad_new_features.new_zeros((N, num_c_in))
if grouped_idxs.shape[0] > 0:
pointnet2.vector_pool_grad_wrapper(
grad_new_features.contiguous(), point_cnt_of_grid, grouped_idxs,
grad_support_features
)
return None, None, grad_support_features, None, None, None, None, None, None, None, None, None, None, None, None
vector_pool_with_voxel_query_op = VectorPoolWithVoxelQuery.apply
if __name__ == '__main__':
pass